Name
Abstract | ||
|---|---|---|
Mid-level semantic attributes have obtained some success in image retrieval and re-ranking. However, due to the semantic gap between the low-level feature and intermediate semantic concept, information loss is considerable in the process of converting the low-level feature to semantic concept. To tackle this problem, we tried to bridge the semantic gap by looking for the complementary of different mid-level features. In this paper, a framework is proposed to improve image re-ranking by fusing multiple mid-level features together. The framework contains three mid-level features (DCNN-ImageNet attributes, Fisher vector, sparse coding spatial pyramid matching) and a semi-supervised multigraph-based model that combines these features together. In addition, our framework can be easily extended to utilize arbitrary number of features for image re-ranking. The experiments are conducted on the a-Pascal dataset, and our approach that fuses different features together is able to boost performance of image re-ranking efficiently. © 2014, Springer-Verlag Berlin Heidelberg. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s00530-014-0448-z | Multimedia Systems |
Keywords | Field | DocType |
Image retrieval,Multiple feature fusion,Re-ranking | Semantic similarity,Ranking,Feature detection (computer vision),Pattern recognition,Neural coding,Computer science,Semantic gap,Image retrieval,Artificial intelligence,Pyramid,Semantic computing | Journal |
Volume | Issue | ISSN |
23 | 1 | 09424962 |
Citations | PageRank | References |
1 | 0.36 | 36 |
Authors | ||
6 | ||